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A curated list of action recognition and related area resources
| Date | Stars |
|---|---|
| 2026-07-24 | 4010 |
| 2026-07-25 | 4010 |
| 2026-07-28 | 4010 |
| 2026-07-30 | 4010 |
| 2026-08-06 | 4010 |
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# Awesome Action Recognition: [](https://github.com/sindresorhus/awesome) A curated list of action recognition and related area (e.g. object recognition, pose estimation) resources, inspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision). ## Contents - [Action Recognition and Video Understanding](#action-recognition-and-video-understanding) - [Object Recognition](#object-recognition) - [Pose Estimation](#pose-estimation) - [Competitions](#competitions) ## Action Recognition and Video Understanding ### Summary posts * [Deep Learning for Videos: A 2018 Guide to Action Recognition](http://blog.qure.ai/notes/deep-learning-for-videos-action-recognition-review) - Summary of major landmark action recognition research papers till 2018 * [Literature Survey: Human Action Recognition](https://towardsdatascience.com/literature-survey-human-action-recognition-cc7c3818a99a) - Brief human action recognition literature survey of work published between 2014 and 2019. ### Video Representation * [Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition](https://papers.nips.cc/paper/8372-why-cant-i-dance-in-the-mall-learning-to-mitigate-scene-bias-in-action-recognition.pdf) - J. Choi et al., NeurIPS2019. [[project web]](http://chengao.vision/SDN/) [[code]](https://github.com/vt-vl-lab/SDN) [[arXiv]](https://arxiv.org/abs/1912.05534) * [SlowFast Networks for Video Recognition](https://arxiv.org/abs/1812.03982) - C. Feichtenhofer et al., ICCV2019. [[code]](https://github.com/facebookresearch/SlowFast) * [Large-scale weakly-supervised pre-training for video action recognition](https://research.fb.com/wp-content/uploads/2019/05/Large-scale-weakly-supervised-pre-training-for-video-action-recognition.pdf?) - D. Ghadiyaram et al., arXiv2019. * [Video Classification with Channel-Separated Convolutional Networks](https://arxiv.org/pdf/1904.02811.pdf) - D. Tran et al., arXiv2019. * [DistInit: Learning Video Representations without a Single Labeled Video](https://arxiv.org/pdf/1901.09244.pdf) - R. Girdhar et al., arXiv2019. * [SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition](https://arxiv.org/pdf/1904.04289.pdf) - B. Korbar et al., arXiv2019. * [Video Action Transformer Network](https://arxiv.org/pdf/1812.02707.pdf) - R. Girdhar et al., CVPR2019. [[project web]](https://rohitgirdhar.github.io/ActionTransformer/) * [Learning Correspondence from the Cycle-consistency of Time](https://arxiv.org/pdf/1903.07593.pdf) - X. Wang et al., CVPR2019. [[code]](https://github.com/xiaolonw/TimeCycle) [[project web]](https://ajabri.github.io/timecycle/) * [Representation Flow for Action Recognition](https://arxiv.org/pdf/1810.01455.pdf) - AJ. Piergiovanni and M. S. Ryoo et al., CVPR2019. * [Collaborative Spatiotemporal Feature Learning for Video Action Recognition](https://arxiv.org/pdf/1903.01197.pdf) - C. Li et al., CVPR2019. * [Learning Video Representations from Correspondence Proposals](https://arxiv.org/pdf/1905.07853.pdf) - X. Liu et al., CVPR2019. * [Timeception for Complex Action Recognition](https://arxiv.org/pdf/1812.01289.pdf) - N. Hussein et al., CVPR2019. * [The Visual Centrifuge: Model-Free Layered Video Representations](https://arxiv.org/pdf/1812.01461.pdf) - J.-B. Alayrac et al., CVPR2019. * [Long-Term Feature Banks for Detailed Video Understanding](https://arxiv.org/pdf/1812.05038.pdf) - C.-Y. Wu. et al., CVPR2019. [[code]](https://github.com/facebookresearch/video-long-term-feature-banks/) * [Temporal Relational Reasoning in Videos](https://arxiv.org/pdf/1711.08496.pdf) - B. Zhou et al., ECCV2018. [[code]](https://github.com/metalbubble/TRN-pytorch) [[project web]](http://relation.csail.mit.edu/) * [Action Recognition Zoo](https://github.com/coderSkyChen/Action_Recognition_Zoo) - Codes for popular action recognition models, written based on
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University of Oxford
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Anirudh Thatipelli · PhD, University of Central Florida · United States
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Jeff Alstott
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Valeriu Lacatusu · @Meta · France
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Victor Escorcia · Samsung AI · United Kingdom
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Amir Hossein Karami
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Fred Fang · MIT · Morocco
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Yi Zhu · United States
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Steffen Schneider · @dynamical-inference @ki-macht-schule @kinematik-ai · Germany
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Luke Reichold · Reikam Labs
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Gil Levi
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Arka Sadhu · Meta · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2b895326087eb763, topic:awesome, topic:awesome-list, desc:curated list
matched fp:2b895326087eb763, topic:pose-estimation, readme:computer vision, readme:pose estimation